BDDN: bayesian dynamic differential network analysis in cancer proteomics.

motivationCancer progression and treatment responses are governed by intricate and dynamic molecular interactions. Although differential network analysis offers considerable potential for identifying condition-specific changes in protein-protein interactions, existing methods primarily rely on static comparisons between groups and do not adequately model underlying biological dynamics. This limitation restricts the ability to detect gradual and complex molecular responses to therapeutic interventions.

resultsWe propose a Bayesian dynamic differential network model to infer time-resolved changes in protein-protein interactions. Applied to cancer proteomics data, our approach captures gradual shifts in differential protein-protein interactions between experimental groups that standard group-based approaches fail to detect. The inferred differential networks reveal protein pairs with time-varying interaction patterns between groups, highlighting critical changes associated with drug response. Subsequent analyses, including functional clustering and hub identification, uncover distinct trajectories among differential edges and pinpoint key proteins that mediate pivotal transitions in the dynamic structure of the differential networks.

conclusionsThe proposed Bayesian dynamic differential network model successfully characterizes temporal variations in protein–protein interactions following drug intervention. The method uncovers time-dependent interaction patterns that differ between experimental groups, providing enhanced insights into drug-induced molecular mechanisms. This framework facilitates the identification of critical regulatory proteins and demonstrates broad applicability across diverse time-course omics investigations.